Overview

Dataset statistics

Number of variables23
Number of observations8950
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory184.0 B

Variable types

Categorical2
Numeric21

Alerts

cust_id has a high cardinality: 8950 distinct valuesHigh cardinality
balance is highly overall correlated with balance_frequency and 4 other fieldsHigh correlation
balance_frequency is highly overall correlated with balance and 1 other fieldsHigh correlation
purchases is highly overall correlated with oneoff_purchases and 7 other fieldsHigh correlation
oneoff_purchases is highly overall correlated with purchases and 4 other fieldsHigh correlation
installments_purchases is highly overall correlated with purchases and 4 other fieldsHigh correlation
cash_advance is highly overall correlated with balance and 3 other fieldsHigh correlation
purchases_frequency is highly overall correlated with purchases and 4 other fieldsHigh correlation
oneoff_purchases_frequency is highly overall correlated with purchases and 5 other fieldsHigh correlation
purchases_installments_frequency is highly overall correlated with purchases and 4 other fieldsHigh correlation
cash_advance_frequency is highly overall correlated with balance and 2 other fieldsHigh correlation
cash_advance_trx is highly overall correlated with balance and 2 other fieldsHigh correlation
purchases_trx is highly overall correlated with purchases and 6 other fieldsHigh correlation
minimum_payments is highly overall correlated with balance and 1 other fieldsHigh correlation
avg_ticket_purchases is highly overall correlated with purchases and 3 other fieldsHigh correlation
avg_ticket_expenses is highly overall correlated with cash_advanceHigh correlation
credit_limit_rate is highly overall correlated with purchases and 7 other fieldsHigh correlation
one_payment is highly overall correlated with oneoff_purchases_frequencyHigh correlation
cust_id is uniformly distributedUniform
cust_id has unique valuesUnique
purchases has 2044 (22.8%) zerosZeros
oneoff_purchases has 4302 (48.1%) zerosZeros
installments_purchases has 3916 (43.8%) zerosZeros
cash_advance has 4628 (51.7%) zerosZeros
purchases_frequency has 2043 (22.8%) zerosZeros
oneoff_purchases_frequency has 4302 (48.1%) zerosZeros
purchases_installments_frequency has 3915 (43.7%) zerosZeros
cash_advance_frequency has 4628 (51.7%) zerosZeros
cash_advance_trx has 4628 (51.7%) zerosZeros
purchases_trx has 2044 (22.8%) zerosZeros
payments has 240 (2.7%) zerosZeros
minimum_payments has 313 (3.5%) zerosZeros
prc_full_payment has 5903 (66.0%) zerosZeros
avg_ticket_purchases has 2044 (22.8%) zerosZeros

Reproduction

Analysis started2023-03-08 13:45:40.241071
Analysis finished2023-03-08 13:47:00.611662
Duration1 minute and 20.37 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

cust_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
C10001
 
1
C16135
 
1
C16129
 
1
C16130
 
1
C16131
 
1
Other values (8945)
8945 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters53700
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005

Common Values

ValueCountFrequency (%)
C10001 1
 
< 0.1%
C16135 1
 
< 0.1%
C16129 1
 
< 0.1%
C16130 1
 
< 0.1%
C16131 1
 
< 0.1%
C16132 1
 
< 0.1%
C16133 1
 
< 0.1%
C16134 1
 
< 0.1%
C16136 1
 
< 0.1%
C16144 1
 
< 0.1%
Other values (8940) 8940
99.9%

Length

2023-03-08T10:47:00.708287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c10001 1
 
< 0.1%
c10011 1
 
< 0.1%
c10017 1
 
< 0.1%
c10016 1
 
< 0.1%
c10015 1
 
< 0.1%
c10014 1
 
< 0.1%
c10013 1
 
< 0.1%
c10012 1
 
< 0.1%
c10010 1
 
< 0.1%
c10034 1
 
< 0.1%
Other values (8940) 8940
99.9%

Most occurring characters

ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44750
83.3%
Uppercase Letter 8950
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12672
28.3%
0 3737
 
8.4%
2 3652
 
8.2%
3 3651
 
8.2%
5 3642
 
8.1%
7 3640
 
8.1%
4 3636
 
8.1%
6 3633
 
8.1%
8 3633
 
8.1%
9 2854
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
C 8950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44750
83.3%
Latin 8950
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12672
28.3%
0 3737
 
8.4%
2 3652
 
8.2%
3 3651
 
8.2%
5 3642
 
8.1%
7 3640
 
8.1%
4 3636
 
8.1%
6 3633
 
8.1%
8 3633
 
8.1%
9 2854
 
6.4%
Latin
ValueCountFrequency (%)
C 8950
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

balance
Real number (ℝ)

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.4748
Minimum0
Maximum19043.139
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:00.860768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8145184
Q1128.28192
median873.38523
Q32054.14
95-th percentile5909.1118
Maximum19043.139
Range19043.139
Interquartile range (IQR)1925.8581

Descriptive statistics

Standard deviation2081.5319
Coefficient of variation (CV)1.3304988
Kurtosis7.6747513
Mean1564.4748
Median Absolute Deviation (MAD)799.8652
Skewness2.393386
Sum14002050
Variance4332775
MonotonicityNot monotonic
2023-03-08T10:47:01.016759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
0.9%
40.900749 1
 
< 0.1%
1213.551338 1
 
< 0.1%
1253.188317 1
 
< 0.1%
5058.299635 1
 
< 0.1%
296.905944 1
 
< 0.1%
1084.652647 1
 
< 0.1%
237.198442 1
 
< 0.1%
1636.518315 1
 
< 0.1%
468.851415 1
 
< 0.1%
Other values (8861) 8861
99.0%
ValueCountFrequency (%)
0 80
0.9%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.006651 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.01968 1
 
< 0.1%
0.021102 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

balance_frequency
Real number (ℝ)

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87727073
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:01.222751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.236904
Coefficient of variation (CV)0.27004663
Kurtosis3.0923696
Mean0.87727073
Median Absolute Deviation (MAD)0
Skewness-2.0232655
Sum7851.573
Variance0.056123506
MonotonicityNot monotonic
2023-03-08T10:47:01.384428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.818182 278
 
3.1%
0.727273 223
 
2.5%
0.545455 219
 
2.4%
0.636364 209
 
2.3%
0.454545 172
 
1.9%
0.363636 170
 
1.9%
0.272727 151
 
1.7%
0.181818 146
 
1.6%
Other values (33) 761
 
8.5%
ValueCountFrequency (%)
0 80
0.9%
0.090909 67
0.7%
0.1 8
 
0.1%
0.111111 5
 
0.1%
0.125 9
 
0.1%
0.142857 7
 
0.1%
0.166667 7
 
0.1%
0.181818 146
1.6%
0.2 9
 
0.1%
0.222222 5
 
0.1%
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.6%
0.857143 51
 
0.6%
0.833333 60
 
0.7%
0.818182 278
 
3.1%
0.8 20
 
0.2%
0.777778 22
 
0.2%

purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.2048
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:01.544959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.6348
Coefficient of variation (CV)2.1298091
Kurtosis111.38877
Mean1003.2048
Median Absolute Deviation (MAD)361.28
Skewness8.1442691
Sum8978683.3
Variance4565208.2
MonotonicityNot monotonic
2023-03-08T10:47:01.759628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
 
22.8%
45.65 27
 
0.3%
60 16
 
0.2%
150 16
 
0.2%
300 13
 
0.1%
200 13
 
0.1%
100 13
 
0.1%
450 12
 
0.1%
50 10
 
0.1%
600 10
 
0.1%
Other values (6193) 6776
75.7%
ValueCountFrequency (%)
0 2044
22.8%
0.01 4
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 2
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

oneoff_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.43737
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:01.933740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.8879
Coefficient of variation (CV)2.8017948
Kurtosis164.18757
Mean592.43737
Median Absolute Deviation (MAD)38
Skewness10.045083
Sum5302314.5
Variance2755227.9
MonotonicityNot monotonic
2023-03-08T10:47:02.125647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4302
48.1%
45.65 46
 
0.5%
50 17
 
0.2%
200 15
 
0.2%
60 13
 
0.1%
100 13
 
0.1%
150 12
 
0.1%
70 12
 
0.1%
1000 12
 
0.1%
250 11
 
0.1%
Other values (4004) 4497
50.2%
ValueCountFrequency (%)
0 4302
48.1%
0.01 7
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 4
 
< 0.1%
1.4 2
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

installments_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.06764
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:02.327115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.33812
Coefficient of variation (CV)2.199974
Kurtosis96.575178
Mean411.06764
Median Absolute Deviation (MAD)89
Skewness7.2991199
Sum3679055.4
Variance817827.43
MonotonicityNot monotonic
2023-03-08T10:47:02.508425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3916
43.8%
300 14
 
0.2%
200 14
 
0.2%
100 14
 
0.2%
150 12
 
0.1%
125 11
 
0.1%
75 9
 
0.1%
350 8
 
0.1%
450 8
 
0.1%
500 8
 
0.1%
Other values (4442) 4936
55.2%
ValueCountFrequency (%)
0 3916
43.8%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.28 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

cash_advance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.87111
Minimum0
Maximum47137.212
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:02.706353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.8211
95-th percentile4647.1691
Maximum47137.212
Range47137.212
Interquartile range (IQR)1113.8211

Descriptive statistics

Standard deviation2097.1639
Coefficient of variation (CV)2.1424311
Kurtosis52.899434
Mean978.87111
Median Absolute Deviation (MAD)0
Skewness5.1666091
Sum8760896.5
Variance4398096.3
MonotonicityNot monotonic
2023-03-08T10:47:03.076379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
495.425832 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
2462.100789 1
 
< 0.1%
Other values (4313) 4313
48.2%
ValueCountFrequency (%)
0 4628
51.7%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

purchases_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49035055
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:03.302673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40137075
Coefficient of variation (CV)0.81853839
Kurtosis-1.6386309
Mean0.49035055
Median Absolute Deviation (MAD)0.416667
Skewness0.060164236
Sum4388.6374
Variance0.16109848
MonotonicityNot monotonic
2023-03-08T10:47:03.509412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2178
24.3%
0 2043
22.8%
0.083333 677
 
7.6%
0.916667 396
 
4.4%
0.5 395
 
4.4%
0.166667 392
 
4.4%
0.833333 373
 
4.2%
0.333333 367
 
4.1%
0.25 345
 
3.9%
0.583333 316
 
3.5%
Other values (37) 1468
16.4%
ValueCountFrequency (%)
0 2043
22.8%
0.083333 677
 
7.6%
0.090909 43
 
0.5%
0.1 27
 
0.3%
0.111111 18
 
0.2%
0.125 32
 
0.4%
0.142857 26
 
0.3%
0.166667 392
 
4.4%
0.181818 16
 
0.2%
0.2 19
 
0.2%
ValueCountFrequency (%)
1 2178
24.3%
0.916667 396
 
4.4%
0.909091 28
 
0.3%
0.9 24
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 25
 
0.3%
0.833333 373
 
4.2%
0.818182 21
 
0.2%
0.8 9
 
0.1%

oneoff_purchases_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20245768
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:03.686742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.29833607
Coefficient of variation (CV)1.4735725
Kurtosis1.1618456
Mean0.20245768
Median Absolute Deviation (MAD)0.083333
Skewness1.5356128
Sum1811.9963
Variance0.089004408
MonotonicityNot monotonic
2023-03-08T10:47:03.870919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.166667 592
 
6.6%
1 481
 
5.4%
0.25 418
 
4.7%
0.333333 355
 
4.0%
0.416667 244
 
2.7%
0.5 235
 
2.6%
0.583333 197
 
2.2%
0.666667 167
 
1.9%
Other values (37) 855
 
9.6%
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.090909 56
 
0.6%
0.1 39
 
0.4%
0.111111 26
 
0.3%
0.125 41
 
0.5%
0.142857 37
 
0.4%
0.166667 592
 
6.6%
0.181818 34
 
0.4%
0.2 27
 
0.3%
ValueCountFrequency (%)
1 481
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 120
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

purchases_installments_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36443734
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:04.079574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39744778
Coefficient of variation (CV)1.0905792
Kurtosis-1.3986322
Mean0.36443734
Median Absolute Deviation (MAD)0.166667
Skewness0.50920116
Sum3261.7142
Variance0.15796474
MonotonicityNot monotonic
2023-03-08T10:47:04.254236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3915
43.7%
1 1331
 
14.9%
0.416667 388
 
4.3%
0.916667 345
 
3.9%
0.833333 311
 
3.5%
0.5 310
 
3.5%
0.166667 305
 
3.4%
0.666667 292
 
3.3%
0.75 291
 
3.3%
0.083333 275
 
3.1%
Other values (37) 1187
 
13.3%
ValueCountFrequency (%)
0 3915
43.7%
0.083333 275
 
3.1%
0.090909 12
 
0.1%
0.1 6
 
0.1%
0.111111 9
 
0.1%
0.125 5
 
0.1%
0.142857 6
 
0.1%
0.166667 305
 
3.4%
0.181818 14
 
0.2%
0.2 9
 
0.1%
ValueCountFrequency (%)
1 1331
14.9%
0.916667 345
 
3.9%
0.909091 25
 
0.3%
0.9 19
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 30
 
0.3%
0.833333 311
 
3.5%
0.818182 21
 
0.2%
0.8 18
 
0.2%

cash_advance_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:04.471706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.20012139
Coefficient of variation (CV)1.4807989
Kurtosis3.3347343
Mean0.1351442
Median Absolute Deviation (MAD)0
Skewness1.8286863
Sum1209.5406
Variance0.04004857
MonotonicityNot monotonic
2023-03-08T10:47:04.651426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.166667 759
 
8.5%
0.25 578
 
6.5%
0.333333 439
 
4.9%
0.416667 273
 
3.1%
0.5 215
 
2.4%
0.583333 142
 
1.6%
0.666667 125
 
1.4%
0.090909 70
 
0.8%
Other values (44) 700
 
7.8%
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.090909 70
 
0.8%
0.1 39
 
0.4%
0.111111 29
 
0.3%
0.125 47
 
0.5%
0.142857 49
 
0.5%
0.166667 759
 
8.5%
0.181818 42
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 25
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

cash_advance_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2488268
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:04.869528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8246467
Coefficient of variation (CV)2.1006496
Kurtosis61.646862
Mean3.2488268
Median Absolute Deviation (MAD)0
Skewness5.7212982
Sum29077
Variance46.575803
MonotonicityNot monotonic
2023-03-08T10:47:05.050348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
10 150
 
1.7%
Other values (55) 915
 
10.2%
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
9 111
 
1.2%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

purchases_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.709832
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:05.243020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.857649
Coefficient of variation (CV)1.6898662
Kurtosis34.7931
Mean14.709832
Median Absolute Deviation (MAD)7
Skewness4.6306553
Sum131653
Variance617.90272
MonotonicityNot monotonic
2023-03-08T10:47:05.451085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
12 570
 
6.4%
2 379
 
4.2%
6 352
 
3.9%
3 314
 
3.5%
4 285
 
3.2%
7 275
 
3.1%
5 267
 
3.0%
8 267
 
3.0%
Other values (163) 3530
39.4%
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
2 379
 
4.2%
3 314
 
3.5%
4 285
 
3.2%
5 267
 
3.0%
6 352
 
3.9%
7 275
 
3.1%
8 267
 
3.0%
9 248
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

credit_limit
Real number (ℝ)

Distinct206
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4493.9473
Minimum0
Maximum30000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:05.679488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range30000
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.9225
Coefficient of variation (CV)0.80973859
Kurtosis2.8364188
Mean4493.9473
Median Absolute Deviation (MAD)1800
Skewness1.5223633
Sum40220828
Variance13241757
MonotonicityNot monotonic
2023-03-08T10:47:05.865290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 784
 
8.8%
1500 722
 
8.1%
1200 621
 
6.9%
1000 614
 
6.9%
2500 612
 
6.8%
4000 506
 
5.7%
6000 463
 
5.2%
5000 389
 
4.3%
2000 371
 
4.1%
7500 277
 
3.1%
Other values (196) 3591
40.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 121
1.4%
600 21
 
0.2%
650 1
 
< 0.1%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

payments
Real number (ℝ)

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.1439
Minimum0
Maximum50721.483
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:06.049271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.988924
Q1383.27617
median856.90155
Q31901.1343
95-th percentile6082.0906
Maximum50721.483
Range50721.483
Interquartile range (IQR)1517.8582

Descriptive statistics

Standard deviation2895.0638
Coefficient of variation (CV)1.6704117
Kurtosis54.770736
Mean1733.1439
Median Absolute Deviation (MAD)581.35146
Skewness5.9076198
Sum15511637
Variance8381394.2
MonotonicityNot monotonic
2023-03-08T10:47:06.255392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 240
 
2.7%
201.802084 1
 
< 0.1%
398.316441 1
 
< 0.1%
826.036748 1
 
< 0.1%
2571.573214 1
 
< 0.1%
1903.279643 1
 
< 0.1%
454.888506 1
 
< 0.1%
956.028747 1
 
< 0.1%
4560.77572 1
 
< 0.1%
1825.349955 1
 
< 0.1%
Other values (8701) 8701
97.2%
ValueCountFrequency (%)
0 240
2.7%
0.049513 1
 
< 0.1%
0.056466 1
 
< 0.1%
2.389583 1
 
< 0.1%
3.500505 1
 
< 0.1%
4.523555 1
 
< 0.1%
4.841543 1
 
< 0.1%
5.070726 1
 
< 0.1%
9.040017 1
 
< 0.1%
9.533313 1
 
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

minimum_payments
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8637
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean833.98345
Minimum0
Maximum76406.208
Zeros313
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:06.478385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.781837
Q1163.02816
median289.6284
Q3788.7135
95-th percentile2719.5669
Maximum76406.208
Range76406.208
Interquartile range (IQR)625.68534

Descriptive statistics

Standard deviation2335.9899
Coefficient of variation (CV)2.8010027
Kurtosis292.35773
Mean833.98345
Median Absolute Deviation (MAD)188.72465
Skewness13.80843
Sum7464151.9
Variance5456848.9
MonotonicityNot monotonic
2023-03-08T10:47:06.859883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 313
 
3.5%
299.351881 2
 
< 0.1%
150.317143 1
 
< 0.1%
271.528169 1
 
< 0.1%
6404.855484 1
 
< 0.1%
616.862544 1
 
< 0.1%
211.984193 1
 
< 0.1%
324.954747 1
 
< 0.1%
1600.26917 1
 
< 0.1%
277.546713 1
 
< 0.1%
Other values (8627) 8627
96.4%
ValueCountFrequency (%)
0 313
3.5%
0.019163 1
 
< 0.1%
0.037744 1
 
< 0.1%
0.05588 1
 
< 0.1%
0.059481 1
 
< 0.1%
0.117036 1
 
< 0.1%
0.261984 1
 
< 0.1%
0.311953 1
 
< 0.1%
0.319475 1
 
< 0.1%
1.113027 1
 
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

prc_full_payment
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15371465
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:07.057494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924992
Coefficient of variation (CV)1.9028713
Kurtosis2.4323953
Mean0.15371465
Median Absolute Deviation (MAD)0
Skewness1.9428199
Sum1375.7461
Variance0.08555578
MonotonicityNot monotonic
2023-03-08T10:47:07.243847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5903
66.0%
1 488
 
5.5%
0.083333 426
 
4.8%
0.166667 166
 
1.9%
0.5 156
 
1.7%
0.25 156
 
1.7%
0.090909 153
 
1.7%
0.333333 134
 
1.5%
0.1 94
 
1.1%
0.2 83
 
0.9%
Other values (37) 1191
 
13.3%
ValueCountFrequency (%)
0 5903
66.0%
0.083333 426
 
4.8%
0.090909 153
 
1.7%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.8%
0.2 83
 
0.9%
ValueCountFrequency (%)
1 488
5.5%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

tenure
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.517318
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:07.390480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3383308
Coefficient of variation (CV)0.11620159
Kurtosis7.6948232
Mean11.517318
Median Absolute Deviation (MAD)0
Skewness-2.9430173
Sum103080
Variance1.7911292
MonotonicityNot monotonic
2023-03-08T10:47:07.515891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
6 204
 
2.3%
8 196
 
2.2%
7 190
 
2.1%
9 175
 
2.0%
ValueCountFrequency (%)
6 204
 
2.3%
7 190
 
2.1%
8 196
 
2.2%
9 175
 
2.0%
10 236
 
2.6%
11 365
 
4.1%
12 7584
84.7%
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
9 175
 
2.0%
8 196
 
2.2%
7 190
 
2.1%
6 204
 
2.3%

one_payment
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
1
4648 
0
4302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

Length

2023-03-08T10:47:07.692214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T10:47:07.854509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

Most occurring characters

ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8950
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4648
51.9%
0 4302
48.1%

avg_ticket_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6192
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.875711
Minimum0
Maximum5981.6667
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:07.998025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median41.428333
Q378.761889
95-th percentile229
Maximum5981.6667
Range5981.6667
Interquartile range (IQR)66.761889

Descriptive statistics

Standard deviation193.5529
Coefficient of variation (CV)2.5509204
Kurtosis414.25875
Mean75.875711
Median Absolute Deviation (MAD)34.098468
Skewness16.065324
Sum679087.61
Variance37462.726
MonotonicityNot monotonic
2023-03-08T10:47:08.165434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
 
22.8%
50 30
 
0.3%
45.65 27
 
0.3%
60 22
 
0.2%
100 17
 
0.2%
25 16
 
0.2%
40 15
 
0.2%
20 11
 
0.1%
90 11
 
0.1%
200 10
 
0.1%
Other values (6182) 6747
75.4%
ValueCountFrequency (%)
0 2044
22.8%
0.01 4
 
< 0.1%
0.05 1
 
< 0.1%
0.7 2
 
< 0.1%
0.7 3
 
< 0.1%
1 3
 
< 0.1%
1.48 1
 
< 0.1%
2 1
 
< 0.1%
2.783333333 1
 
< 0.1%
2.856 1
 
< 0.1%
ValueCountFrequency (%)
5981.666667 4
< 0.1%
2900 1
 
< 0.1%
2600 1
 
< 0.1%
2523.438 1
 
< 0.1%
2483.26 1
 
< 0.1%
2190 1
 
< 0.1%
2000 2
< 0.1%
1875 1
 
< 0.1%
1865.044 1
 
< 0.1%
1810 1
 
< 0.1%

avg_ticket_expenses
Real number (ℝ)

Distinct8556
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.57554
Minimum0
Maximum10590.411
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:08.356415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.2145
Q143.681591
median85.455
Q3198.06602
95-th percentile764.4195
Maximum10590.411
Range10590.411
Interquartile range (IQR)154.38443

Descriptive statistics

Standard deviation485.07297
Coefficient of variation (CV)2.3145495
Kurtosis184.94346
Mean209.57554
Median Absolute Deviation (MAD)53.089751
Skewness11.152546
Sum1875701.1
Variance235295.79
MonotonicityNot monotonic
2023-03-08T10:47:08.542694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 19
 
0.2%
45.65 19
 
0.2%
60 13
 
0.1%
40 10
 
0.1%
100 10
 
0.1%
30 8
 
0.1%
25 8
 
0.1%
20 8
 
0.1%
45 7
 
0.1%
35 7
 
0.1%
Other values (8546) 8841
98.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.01 2
< 0.1%
0.7 2
< 0.1%
2.783333333 1
< 0.1%
2.856 1
< 0.1%
3.093333333 1
< 0.1%
3.66 1
< 0.1%
4.018 1
< 0.1%
4.494244429 1
< 0.1%
4.5 1
< 0.1%
ValueCountFrequency (%)
10590.41113 4
< 0.1%
9798.167329 1
 
< 0.1%
9671.336737 1
 
< 0.1%
9553.955906 1
 
< 0.1%
7968.273359 1
 
< 0.1%
7378.253685 1
 
< 0.1%
7275.716281 1
 
< 0.1%
6523.780195 1
 
< 0.1%
6479.639793 1
 
< 0.1%
6466.73381 1
 
< 0.1%

debt_rate
Real number (ℝ)

Distinct8710
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.612492
Minimum0
Maximum916.4779
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:08.742139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.089975135
Q10.39740582
median0.77447348
Q31.1585227
95-th percentile3.5688903
Maximum916.4779
Range916.4779
Interquartile range (IQR)0.76111692

Descriptive statistics

Standard deviation148.2114
Coefficient of variation (CV)5.7866842
Kurtosis32.176496
Mean25.612492
Median Absolute Deviation (MAD)0.37975518
Skewness5.8451695
Sum229231.8
Variance21966.62
MonotonicityNot monotonic
2023-03-08T10:47:08.914872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
916.4778959 241
 
2.7%
0.2795097625 1
 
< 0.1%
0.8624844192 1
 
< 0.1%
0.8377213171 1
 
< 0.1%
0.9448788869 1
 
< 0.1%
0.8358787228 1
 
< 0.1%
0.3040293254 1
 
< 0.1%
0.08712517804 1
 
< 0.1%
1.81995471 1
 
< 0.1%
0.2635416079 1
 
< 0.1%
Other values (8700) 8700
97.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.0007126040409 1
< 0.1%
0.0008395366687 1
< 0.1%
0.001178343126 1
< 0.1%
0.001332318214 1
< 0.1%
0.00137563274 1
< 0.1%
0.001443141501 1
< 0.1%
0.00159981886 1
< 0.1%
0.001676235847 1
< 0.1%
0.002533629151 1
< 0.1%
ValueCountFrequency (%)
916.4778959 241
2.7%
95.24258573 1
 
< 0.1%
32.36969736 1
 
< 0.1%
25.16366669 1
 
< 0.1%
17.0099304 1
 
< 0.1%
14.35301618 1
 
< 0.1%
13.99995818 1
 
< 0.1%
13.60114306 1
 
< 0.1%
12.90542858 1
 
< 0.1%
12.15774644 1
 
< 0.1%

credit_limit_rate
Real number (ℝ)

Distinct8576
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008.7257
Minimum0
Maximum49039.574
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-03-08T10:47:09.085041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.020072467
Q139.842853
median361.62104
Q31110.366
95-th percentile4000.899
Maximum49039.574
Range49039.574
Interquartile range (IQR)1070.5231

Descriptive statistics

Standard deviation2196.1036
Coefficient of variation (CV)2.1771067
Kurtosis125.00243
Mean1008.7257
Median Absolute Deviation (MAD)361.50398
Skewness8.6625925
Sum9028095.3
Variance4822871
MonotonicityNot monotonic
2023-03-08T10:47:09.282007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.65 19
 
0.2%
300 10
 
0.1%
60 9
 
0.1%
114 8
 
0.1%
150 8
 
0.1%
350 7
 
0.1%
125 7
 
0.1%
70 7
 
0.1%
200 7
 
0.1%
120 7
 
0.1%
Other values (8566) 8861
99.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.0003361101111 1
< 0.1%
0.0008802314444 1
< 0.1%
0.001093014222 1
< 0.1%
0.001305855 1
< 0.1%
0.001505852367 1
< 0.1%
0.001535375317 1
< 0.1%
0.001580220778 1
< 0.1%
0.001638377 1
< 0.1%
0.001681454772 1
< 0.1%
ValueCountFrequency (%)
49039.57413 2
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42651 1
< 0.1%
26784.62 1
< 0.1%

Interactions

2023-03-08T10:46:56.513170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:44.723663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.937999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:51.711440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.811653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:00.623462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:04.455459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.297344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.986896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.401103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.046525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:22.632086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.152051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.532032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.849580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.188084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.695018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.994024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.451930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:50.045016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.549680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.668447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:44.903517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:48.084119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:52.034996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.964683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:00.924608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:04.620558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.456601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:12.153518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.554400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.233572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:22.855351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.316462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.693675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:33.030983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.372951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.844950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:43.180369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.642730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:50.249468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.697843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.841526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:45.043768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:48.217976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:52.286124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:56.108427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:01.133334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:04.780805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.616995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:12.296909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.702458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.409370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:23.049360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.458264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.849155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:33.246866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.553676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.981587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:43.353805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.805600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:50.435762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.831678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.982569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:45.186063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:48.368406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:52.462730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:56.268039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:01.291399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:05.100950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.771139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:12.462914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.869818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.557929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:23.207047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.783156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.989220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:33.377945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.745052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:40.123765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:43.537990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.951457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:50.575368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.969675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:57.133850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:45.369640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:48.540920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:52.792258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:56.420330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:01.456569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:05.306562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.940972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:12.665340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:16.044103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.725685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:23.389168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.933953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:30.142911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:33.530192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.926727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:40.273479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:43.741501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:47.133869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:50.750959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:54.126943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:57.262468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:45.514748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:48.677985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:53.256427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:56.565225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:01.617187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:05.478397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:09.110470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:12.807057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:16.195479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:19.866972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:23.536031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:27.072067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-08T10:45:58.693376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:03.372405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:07.190937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.037507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:14.453900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:17.903906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:21.571640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:25.186393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:28.604111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:31.845214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:35.304544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:38.761832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.036058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:45.493540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:48.881620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:52.630439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:55.687596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:59.061013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.180966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:50.536431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.100859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:58.969781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:03.537065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:07.373420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.184320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:14.604733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:18.081491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:21.739204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:25.350218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:28.781464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.004938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:35.452126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:38.914630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.170107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:45.632053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:49.021300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:52.776275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:55.837862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:59.201143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.329597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:50.701380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.265815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:59.196561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:03.694512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:07.550463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.326447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:14.781512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:18.254858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:21.907662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:25.519501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:28.931951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.222160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:35.593594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.083504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.304574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:45.770624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:49.176016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:52.956533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:55.973565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:59.350156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.506432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:50.857495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.416578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:59.528356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:03.911369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:07.767410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.500901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:14.939585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:18.581808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:22.082981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:25.689460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.087635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.393548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:35.758407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.239525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.449801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:45.916464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:49.338561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.123555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.122821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:59.484390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.661715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:51.191617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.557436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:59.855721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:04.101020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:07.958397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.652523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.114098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:18.748036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:22.287418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:25.860610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.233712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.550853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:35.915547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.390061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.610476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.067413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:49.500170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.267700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.255416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:59.614522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:47.794845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:51.505402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:45:55.681804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:00.209758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:04.274381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:08.124677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:11.828078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:15.247980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:18.880785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:22.470669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:26.003438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:29.392452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:32.707363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:36.040544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:39.528300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:42.821590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:46.251497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:49.666063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:53.401597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T10:46:56.384706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-08T10:47:09.475941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
balancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenureavg_ticket_purchasesavg_ticket_expensesdebt_ratecredit_limit_rateone_payment
balance1.0000.5450.0060.146-0.0900.566-0.1450.120-0.1440.5440.549-0.0460.3720.4320.882-0.4840.066-0.0680.3930.0540.0070.030
balance_frequency0.5451.0000.1480.1350.1280.1370.2020.1590.1520.1770.1760.2030.1070.2070.528-0.1740.229-0.034-0.033-0.0130.1340.111
purchases0.0060.1481.0000.7510.706-0.3850.7950.6930.606-0.391-0.3840.8850.2610.3950.0150.2380.1330.759-0.1410.2440.9930.167
oneoff_purchases0.1460.1350.7511.0000.200-0.1850.4240.9520.117-0.179-0.1750.5900.3050.3630.0830.0490.0960.6650.0470.2320.7460.143
installments_purchases-0.0900.1280.7060.2001.000-0.3570.7860.1850.923-0.366-0.3570.7840.1240.239-0.0280.2760.1250.356-0.3490.1300.7010.099
cash_advance0.5660.137-0.385-0.185-0.3571.000-0.454-0.189-0.3780.9410.952-0.4080.1630.2570.464-0.266-0.113-0.3440.6070.283-0.3620.033
purchases_frequency-0.1450.2020.7950.4240.786-0.4541.0000.4630.852-0.453-0.4470.9240.1040.172-0.0780.2920.0980.430-0.4830.1360.7890.389
oneoff_purchases_frequency0.1200.1590.6930.9520.185-0.1890.4631.0000.112-0.176-0.1740.6070.2820.3210.0670.0610.0840.568-0.0540.2000.6880.761
purchases_installments_frequency-0.1440.1520.6060.1170.923-0.3780.8520.1121.000-0.382-0.3740.7810.0480.121-0.0630.2590.1140.215-0.4870.0830.6020.086
cash_advance_frequency0.5440.177-0.391-0.179-0.3660.941-0.453-0.176-0.3821.0000.983-0.4070.0880.2030.446-0.287-0.131-0.3510.4780.220-0.3780.123
cash_advance_trx0.5490.176-0.384-0.175-0.3570.952-0.447-0.174-0.3740.9831.000-0.3990.0970.2150.459-0.281-0.099-0.3470.4790.226-0.3690.000
purchases_trx-0.0460.2030.8850.5900.784-0.4080.9240.6070.781-0.407-0.3991.0000.1910.2840.0010.2530.1690.466-0.4400.1710.8790.266
credit_limit0.3720.1070.2610.3050.1240.1630.1040.2820.0480.0880.0970.1911.0000.4500.2570.0210.1710.1860.2530.1640.2560.228
payments0.4320.2070.3950.3630.2390.2570.1720.3210.1210.2030.2150.2840.4501.0000.4210.1870.2050.2430.351-0.0630.4060.082
minimum_payments0.8820.5280.0150.083-0.0280.464-0.0780.067-0.0630.4460.4590.0010.2570.4211.000-0.4060.142-0.0750.260-0.1310.0130.033
prc_full_payment-0.484-0.1740.2380.0490.276-0.2660.2920.0610.259-0.287-0.2810.2530.0210.187-0.4061.0000.0200.143-0.141-0.0240.2450.019
tenure0.0660.2290.1330.0960.125-0.1130.0980.0840.114-0.131-0.0990.1690.1710.2050.1420.0201.0000.044-0.123-0.2090.1180.088
avg_ticket_purchases-0.068-0.0340.7590.6650.356-0.3440.4300.5680.215-0.351-0.3470.4660.1860.243-0.0750.1430.0441.0000.1350.1480.7540.104
avg_ticket_expenses0.393-0.033-0.1410.047-0.3490.607-0.483-0.054-0.4870.4780.479-0.4400.2530.3510.260-0.141-0.1230.1351.0000.352-0.1140.117
debt_rate0.054-0.0130.2440.2320.1300.2830.1360.2000.0830.2200.2260.1710.164-0.063-0.131-0.024-0.2090.1480.3521.0000.2820.027
credit_limit_rate0.0070.1340.9930.7460.701-0.3620.7890.6880.602-0.378-0.3690.8790.2560.4060.0130.2450.1180.754-0.1140.2821.0000.166
one_payment0.0300.1110.1670.1430.0990.0330.3890.7610.0860.1230.0000.2660.2280.0820.0330.0190.0880.1040.1170.0270.1661.000

Missing values

2023-03-08T10:46:59.844143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-08T10:47:00.345984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenureone_paymentavg_ticket_purchasesavg_ticket_expensesdebt_ratecredit_limit_rate
0C1000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012047.70000047.7000000.27951095.400000
1C100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.2222221200.0000001610.7363711.2449240.153403
2C100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.00000012164.43083364.4308330.618857773.170000
3C100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.0000000.0000000.0000001211499.000000852.394009916.4778961499.004573
4C10005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.00000012116.00000016.0000000.01733216.000000
5C100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.000000120166.660000166.6600000.3501901333.280000
6C10007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.000000121110.797031110.7970311.0822047091.010000
7C100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.00000012036.35000036.3500000.360169436.200000
8C100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.000000121172.298000172.2980000.861282861.490000
9C10010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.000000121427.200000427.2000001.0130641281.600000
cust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenureone_paymentavg_ticket_purchasesavg_ticket_expensesdebt_ratecredit_limit_rate
8940C19181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.006098.54000098.5400001.059011591.240000
8941C191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.006042.910000487.2199634.797170214.708434
8942C1918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.256018.88000018.8800000.626646113.280000
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